Articles | Volume 15, issue 23
Geosci. Model Dev., 15, 8765–8784, 2022
https://doi.org/10.5194/gmd-15-8765-2022
Geosci. Model Dev., 15, 8765–8784, 2022
https://doi.org/10.5194/gmd-15-8765-2022
Methods for assessment of models
06 Dec 2022
Methods for assessment of models | 06 Dec 2022

Transfer learning for landslide susceptibility modeling using domain adaptation and case-based reasoning

Zhihao Wang et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-119', Anonymous Referee #1, 25 May 2022
    • AC2: 'Reply on RC1', Zhihao Wang, 30 Sep 2022
  • RC2: 'Comment on gmd-2022-119', Anonymous Referee #2, 12 Sep 2022
    • AC1: 'Reply on RC2', Zhihao Wang, 30 Sep 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Zhihao Wang on behalf of the Authors (30 Sep 2022)  Author's response    Author's tracked changes    Manuscript
ED: Reconsider after major revisions (03 Oct 2022) by Xiaomeng Huang
AR by Zhihao Wang on behalf of the Authors (04 Oct 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (09 Oct 2022) by Xiaomeng Huang
ED: Publish subject to minor revisions (review by editor) (28 Oct 2022) by Xiaomeng Huang
AR by Zhihao Wang on behalf of the Authors (04 Nov 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (16 Nov 2022) by Xiaomeng Huang
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Short summary
A lack of inventory data can be a limiting factor in developing landslide predictive models, which are crucial for supporting hazard policy and decision-making. We show how case-based reasoning and domain adaptation (transfer-learning techniques) can effectively retrieve similar landslide modeling situations for prediction in new data-scarce areas. Using cases in Italy, Austria, and Ecuador, our findings support the application of transfer learning for areas that require rapid model development.